DETAILED ACTION
The Applicant’s response, received 03 August 2026, has been fully considered. The following rejections and/or objections are either reiterated or newly applied. They constitute the complete set presently being applied to the instant application.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-59 are pending.
Claims 15-59 are withdrawn.
Claims 1-14 are rejected.
Priority
There are no domestic or foreign applications for which benefit is claimed.
Therefore, the effective filing date of the claimed invention is 04 October 2022.
Drawings
The objection to the drawings in the Office action mailed 28 May 2026 has been withdrawn in view of the amendment to the specification received 03 August 2026.
Specification
The amendment to the specification received 03 August 2026 has been entered.
Claim Interpretation
The claim limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, in the Office action mailed 28 May 2026 has been maintained in view of the amendment received 03 August 2026.
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation is:
data processing system in claim 1.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
The written description discloses a corresponding structure for the generic placeholder:
data processing system in claim 1, at paragraph [0091] in the Specification and Fig. 8 in the drawings (i.e., a computer system).
If applicant does not intend to have this limitation interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
The amendment received 03 August 2026 has been fully considered, however after further consideration, the rejection of claims 1-14 under 35 U.S.C. 101 in the Office action mailed 28 May 2026 has been maintained with modification in view of the amendment, as noted below.
The rejection has been modified to incorporate the newly amended claim limitations.
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); and (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion).
Subject matter eligibility evaluation in accordance with MPEP 2106.
Eligibility Step 1: Step 1 of the eligibility analysis asks: Is the claim to a process, machine, manufacture or composition of matter?
Claims 1-14 recite a method implemented by a data processing system for outputting one or more models (i.e., a process).
Therefore, these claims are encompassed by the categories of statutory subject matter, and thus, satisfy the subject matter eligibility requirements under step 1.
[Step 1: YES]
Eligibility Step 2A: First it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim.
Independent claim 1 recites the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas:
determining one or more attributes of the plurality of process data items (i.e., mental processes);
responsive to the receiving of the plurality of process data items, selecting one or more machine learning models based on the one or more attributes of the plurality of process data items (i.e., mental processes);
accessing one or more mechanistic models suitable for cell or gene therapy production (i.e., mental processes);
integrating the one or more machine learning models with the one or more mechanistic models to obtain one or more integrated models (i.e., mental processes);
selecting one or more predictive models from the one or more machine learning models, the one or more mechanistic models, and the one or more integrated models (i.e., mental processes);
applying the one or more predictive models to the plurality of process data items with the one or more attributes used to select the one or more machine learning models (i.e., mental processes and mathematical concepts); and
adjusting one or more values of one or more parameters of the one or more predictive models selected to reduce uncertainty in model prediction (i.e., mental processes and mathematical concepts).
Dependent claims 2-6, 9, 12, and 13 further recite the following steps which fall within the mental processes and/or mathematical concepts groupings of abstract ideas, as noted below.
Dependent claim 2 further recites:
the one or more attributes comprise at least one of: nonlinearity; collinearity; nonnormality; or dynamics (i.e., mental processes).
Dependent claim 3 further recites:
the one or more mechanistic models are accessed based on at least one of a physical property, a chemical property, or a biological property of the process (i.e., mental processes).
Dependent claim 4 further recites:
arranging the one or more machine learning models and the one or more mechanistic models in a sequence comprising a first one or more models and a second one or more models (i.e., mental processes); and
obtaining an output of the second one or more models (i.e., mental processes and mathematical concepts).
Dependent claim 5 further recites:
determining a first one or more models and a second one or more models from the one or more machine learning models and the one or more mechanistic models (i.e., mental processes);
constraining a prediction of the first one or more models using the second one or more models (i.e., mental processes and mathematical concepts); and
obtaining an output of the first one or more models (i.e., mental processes and mathematical concepts).
Dependent claim 6 further recites:
using one or more output models (i.e., mental processes and mathematical concepts).
Dependent claim 9 further recites:
using one or more algebraic learning via elastic net (ALVEN) output models (i.e., mental processes and mathematical concepts).
Dependent claim 12 further recites:
uses one or more output models (i.e., mental processes and mathematical concepts).
Dependent claim 13 further recites:
uses one or more output models (i.e., mental processes and mathematical concepts).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pen and paper (e.g., determining one or more attributes of the plurality of process data items), and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas (e.g., applying the one or more predictive models to the plurality of process data items with the one or more attributes used to select the one or more machine learning models) are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind.
Therefore, claims 1-14 recite an abstract idea.
[Step 2A Prong One: YES]
Eligibility Step 2A Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that when examined as a whole integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
The judicial exceptions identified in Eligibility Step 2A Prong One are not integrated into a practical application because of the reasons noted below.
In the instant application, the claims provide additional elements for receiving and storing process data items, and accessing mechanistic models suitable for cell or gene therapy production, however once the data is received and the models accessed, the subsequent steps only perform steps of modifying the models to create a hybrid model and then adjusting the values and/or parameters of the model, and then outputting one or more predictive models with an intended use of causing a development or manufacturing process to produce a therapy in accordance with the one or more predictive models. Thus, the claims do not recite any limitations to which the outputted one or more predictive models is practically applied.
Dependent claims 2 and 3 do not recite any elements in addition to the judicial exception, and thus are part of the judicial exception.
The additional elements in independent claim 1 include:
receiving a plurality of process data items, with a process data item specifying a process for producing a cell or gene therapy (i.e., receiving data);
storing the plurality of process data items (i.e., storing data);
a hardware storage device;
accessing the plurality of data items (i.e., accessing data) using the data processing system (i.e., a computer system);
outputting the one or more predictive models with the one or more adjusted values of the one or more parameters (i.e., outputting data); and
causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters.
The additional elements in dependent claim 4-14 include:
transmitting an output of the first one or more models to the second one or more models (i.e., transmitting data) (claim 4);
transmitting data to the second one or more models (i.e., transmitting data) (claim 4);
transmitting input data to the first one or more models (i.e., transmitting data) (claim 5);
the plurality of process data items is obtained from a cell population of a first type (i.e., obtaining data (claim 6);
causing production of a cell population of a second type wherein the second type is different from the first type (claim 6);
each of the cell population of the first type and the cell population of the second type comprises at least one of heterogeneous cell populations or clonal cell populations (claim 7);
the heterogeneous cell populations have at least one of intracellular heterogeneity cell surface heterogeneity (claim 8);
causing production of a stable cell line (claim 9);
the stable cell line comprises HEK293 cells (claim 10);
a scale of the CGT is within a range of 1 mL per production run to 25,000 L per production run (claim 11);
cells grown for at least one mode of batch; fed-batch; perfusion; continuous; semi-continuous; or hybrid of fed-batch and perfusion (claim 12);
an automated or semi-automated production (claim 13); and
the production is in a closed or semi-closed system (claim 14).
The additional elements of a data processing system (i.e., a computer system) (claim 1); and a hardware storage device (claim 1); invoke a computer and/or computer-related components merely as tools for use in the claimed process, and therefore are not an improvement to computer functionality itself, or an improvement to any other technology or technical field, and thus, do not integrate the judicial exceptions into a practical application (MPEP 2106.04(d)(1)).
The additional elements of receiving a plurality of data items, with a process data item specifying a process for producing a cell or gene therapy (i.e., receiving data) (claim 1); storing the plurality of process data items (i.e., storing data) (claim 1); accessing the plurality of process data items (i.e., accessing data) (claim 1); outputting the one or more predictive models with the one or more adjusted values of the one or more parameters (i.e., outputting data) (claim 1); transmitting an output of the first one or more models to the second one or more models (i.e., transmitting data) (claim 4); transmitting data to the second one or more models (i.e., transmitting data) (claim 4); transmitting input data to the first one or more models (i.e., transmitting data) (claim 5); and the plurality of data items is obtained from a cell population of a first type (i.e., obtaining data (claim 6); are merely pre-solution and/or post-solution activities used in the claimed process – nominal or tangential additions to the claims that do not meaningfully limit the claims, and therefore do not add more than insignificant extra-solution activity to the judicial exceptions (MPEP 2106.05(g)).
The additional elements of causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters (claim 1); causing production of a cell population of a second type wherein the second type is different from the first type (claim 6); each of the cell population of the first type and the cell population of the second type comprises at least one of heterogeneous cell populations or clonal cell populations (claim 7); the heterogeneous cell populations have at least one of intracellular heterogeneity cell surface heterogeneity (claim 8); causing production of a stable cell line (claim 9); the stable cell line comprises HEK293 cells (claim 10); a scale of the CGT is within a range of 1 mL per production run to 25,000 L per production run (claim 11); cells grown for at least one mode of batch; fed-batch; perfusion; continuous; semi-continuous; or hybrid of fed-batch and perfusion (claim 12); an automated or semi-automated production (claim 13); and the production is in a closed or semi-closed system (claim 14); amount to mere instructions to apply an exception, because these types of limitations are equivalent to the words “apply it.” These claim limitations attempt to cover any solution to the one or more predictive models with the one or more adjusted values of the one or more parameters, with no restriction on the output of the one or more predictive models to cause production of a cell line, because the limitation of using one or more predictive models could mean any using any model output, since there is not a restriction, limit, or indication as to what the output model actually comprises and how it relates to the steps of the production of a cell population. Therefore, these additional elements do not integrate the judicial exceptions into a practical application (MPEP 2106.05(f)).
Thus, the additionally recited elements merely invoke a computer and/or computer related components as tools; and/or amount to insignificant extra-solution activity; and/or amount to mere instructions to apply an exception; and as such, when all limitations in claims 1-14 have been considered as a whole (i.e., the analysis takes into consideration all the claim limitations and how those limitations interact and impact each other when evaluating whether the exception is integrated into a practical application), the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application, and therefore claims 1-14 are directed to an abstract idea (MPEP 2106.04(d)).
[Step 2A Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
Dependent claims 2 and 3 do not further recite any elements in addition to the judicial exception(s).
The additional elements recited in independent claim 1 and dependent claims 4-14 are identified above, and carried over from Step 2A Prong Two along with their conclusions for analysis at Step 2B. Any additional element or combination of elements that was considered to be insignificant extra-solution activity at Step 2A Prong Two was re-evaluated at Step 2B, because if such re-evaluation finds that the element is unconventional or otherwise more than what is well-understood, routine, conventional activity in the field, this finding may indicate that the additional element is no longer considered to be insignificant; and all additional elements and combination of elements were evaluated to determine whether any additional elements or combination of elements are other than what is well-understood, routine, conventional activity in the field, or simply append well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, per MPEP 2106.05(d).
The additional elements of a data processing system (i.e., a computer system) (claim 1); and a hardware storage device (claim 1); receiving data (claim 1); storing data (claim 1); accessing data (claim 1); outputting data (claim 1); transmitting data (claims 4 and 5); and obtaining data (claim 6); are conventional computer components and/or functions (see MPEP at 2106.05(b) and 2106.05(d)(II) regarding conventionality of computer components and computer processes).
The additional elements of causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters (claim 1); causing production of a cell population of a second type wherein the second type is different from the first type (claim 6); each of the cell population of the first type and the cell population of the second type comprises at least one of heterogeneous cell populations or clonal cell populations (claim 7); the heterogeneous cell populations have at least one of intracellular heterogeneity cell surface heterogeneity (claim 8); causing production of a stable cell line (claim 9); the stable cell line comprises HEK293 cells (claim 10); a scale of the CGT is within a range of 1 mL per production run to 25,000 L per production run (claim 11); cells grown for at least one mode of batch; fed-batch; perfusion; continuous; semi-continuous; or hybrid of fed-batch and perfusion (claim 12); an automated or semi-automated production (claim 13); and the production is in a closed or semi-closed system (claim 14); are conventional. Evidence of conventionality is shown by:
Tan et al. (“HEK293 cell line as a platform to produce recombinant proteins and viral vectors.” Frontiers in Bioengineering and Biotechnology, 2021, vol. 9, article 796991, pp. 1-9, as cited in the Office action mailed 28 May 2026);
Moutsatsou et al. (“Automation in cell and gene therapy manufacturing: from past to future.” Biotechnology Letters, 2019, vol. 41, pp. 1245-1253, as cited in the Office action mailed 28 May 2026); and
Zalai et al. (“Advanced development strategies for biopharmaceutical cell culture processes.” Current Pharmaceutical Biotechnology, 2015, vol. 16, pp. 983-1001, as cited in the Office action mailed 28 May 2026).
Tan et al. reviews the use of HEK293 cells and its subtypes in the production of biotherapeutics, and compares their usage against other commonly used host cell lines in each category of biotherapeutics and summarizes the factors influencing the choice of host cell lines used (Abstract). Tan et al. shows that since 2015, there have been seven HEK-derived products approved by the FDA, and of these, six are cell and gene therapies where the HEK293 cell line or its derivatives were used in the production of viral vectors (page 1, Introduction; and Table 1). Tan et al. further shows that stable producer lines are preferred for the large-scale production of recombinant proteins, and that the most recent HEK-produced recombinant protein therapeutic was produced in HEK293F cells by transfecting the HEK293F cells with a B-domain deleted human FVIII expression construct, and after stable transfectants were selected, clones exhibiting optimal production were selected for use (page 2, col. 1, bottom, and col. 2, top). Tan et al. further shows various production scales comprising 10 cm cell culture dishes and 2L Erlenmeyer flasks (Tables 2 & 3) and 20L WAVE Bioreactor (Table 4).
Moutsatsou et al. reviews how automation can help address the manufacturing issues arising from the development of large-scale manufacturing processes for modern cell and gene therapy, and summarizes and evaluates the existing automated technologies with applicability in cell and gene therapy (Abstract). Moutsatsou et al. shows that automation can refer to many approaches: automation of one step alone, integration of several steps in one machine (1st generation) or fully automated (2nd generation), and that the term “fully automated” refers to a platform or process which apart from eliminating manual operators for culturing cells, it also eliminates the need for manual transfer of materials from one-unit operation to another (page 1247, col. 2, para. 1). Moutsatsou et al. further shows a closed, automated system (page 1250, col. 1, para. 3); a fully automated production unit for reprogramming, cultivation and differentiation of induced pluripotent stems cells (iPSCs) with a capability to process up to 60 different iPSC lines in parallel (page 1250, col. 1, para. 4); and a fully automated manufacturing and banking of cell therapies that is a fully enclosed platform (page 1250, col. 1, para. 5). Moutsatsou et al. further shows that another important advantage of 2nd generation automated cell manufacturing platforms is the increased flexibility and modularity, particularly hardware modules that are integrated in the platform via agents into the control software using a plug-and-produce approach and software that is adaptable to different applications (i.e., different process conditions or different cells) (page 1250, col. 2, para. 4).
Zalai et al. reviews advanced development strategies for biopharmaceutical cell culture processes, with a focus on tools which enable the integration of physiological knowledge into cell culture process development (Abstract) and shows that mechanistic models are mathematical formulations of the internal operation of systems in terms of their constituent parts and mechanisms (page 991, col. 1, para. 4) and further shows a model that has been enhanced by considering different subpopulations, so that monoclonal antibody production could be described after the exponential phase, and that when combined with real-time measurements of extracellular metabolites, such a model could potentially provide real-time information regarding the apoptotic state of different sub-populations (page 991, col. 2, para. 3). Zalai et al. further reviews technological process control strategies (page 994, col. 1, para. 2) including: batch process mode (page 994, col. 1, para. 3); fed-batch process (page 994, col. 1, para. 4); and continuous/perfusion process (page 994, col. 1, paras. 5-6).
Therefore, when taken alone (i.e., individually), all additional elements in claims 1-14 do not amount to significantly more than the above-identified judicial exception(s). Even when evaluated as an ordered combination, the additional elements fail to transform the exception(s) into a patent-eligible application of that exception. Thus, claims 1-14 are deemed to not contribute an inventive concept, i.e., amount to significantly more than the judicial exception(s) (MPEP 2106.05(II)).
[Step 2B: NO]
Response to Arguments
The Applicant’s arguments/remarks received 03 August 2026 have been fully considered, but are not persuasive.
The Applicant states on page 7 (para. 2) of the Remarks that claim 1 requires the features of “causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters.” The Applicant further states that the claims are directed to patent eligible subject matter under at least Step 2A Prong Two, and points to the 2019 Guidance (pp. 12, 15) and further states (para. 3) that as described in the 2019 Guidance, under Step 2A Prong Two, a claim reciting a judicial exception is nevertheless directed to patent eligible subject matter if it integrates a judicial exception into a practical application, such as if it includes an additional element that (i) reflects “an improvement to other technology or technical field,” or (ii) implements “the judicial exception with a particular machine or manufacture.” The Applicant further states (para. 4) that such improvements are recognized by the USPTO and the courts as indicative of patent eligible subject matter. The Applicant further points the USPTO July 2015 Update Appendix 1: Examples (Example 25: noting that Example 25 explains that claims directed to a mathematical relationship nevertheless recite patent eligible subject matter, at least in part because the claimed subject matter improves another technique or field, such as the field of precision rubber molding). The Applicant further states that indeed, even if the claim is directed to an abstract idea, as in Diamond, the claimed subject matter nevertheless applies the one or more predictive models with the one of more adjusted values of the one or more parameters to directly improve upon an industrial process (e.g., by causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters), thus both (i) reflecting an improvement to a technical field, and (ii) implementing the alleged judicial exception with a particular machine or manufacture. The Applicant further states on page 8 (para. 2) that even if viewed strictly as a manufacturing process, claim 1 improves the technical filed of biomanufacturing by directly causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters to reduce uncertainty in model prediction. The Applicant further states that as established in Diamond, utilizing computational or mathematical steps to actively guide and control a physical manufacturing process integrates those steps into an eligible practical application. The Applicant further states (para. 3) that as such, even if the claims were directed to a judicial exception, the claims would nevertheless be directed to patent eligible subject matter, at least because the claims include additional claim elements that reflect an improvement to a technical field and also include elements that implement that judicial exception with, or use that judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim.
These arguments/remarks are not persuasive, because first, regarding the Applicant’s argument that the instant claims implement ‘the judicial exception with a particular machine or manufacture,’ it is noted that the machine-or-transformation test is a useful and important clue, and investigative tool for determining whether a claim is patent eligible under 35 U.S.C. 101, however, it is not a stand-alone test for eligibility. Nonetheless, it is further noted that neither the instant claims nor the arguments/remarks actually identify the particularity of the elements of the machine or apparatus being argued, i.e., the particular machine or apparatus is not specifically identified. Second, regarding the Applicant’s argument that the claims ‘reflect an improvement to a technical field,’ it is noted that the claimed subject matter does not actually apply ‘the one or more predictive models with the one or more adjusted values of the one or more parameters’ to directly improve upon an industrial process, because amended claim 1 only recites an intended use of the limitation “causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters,” and therefore, since the there are no additional elements to which the judicial exceptions are practically applied to, (i.e., the claim does not apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception), any purported improvement is at most an improvement to the recited abstract idea, i.e., the one or more predictive models with the one or more adjusted values of the one or more parameters. Third, regarding the Applicant’s attempt to analogize the instant claims with Diamond v. Diehr, (and also hypothetical Example 25 which is based on Diamond v. Diehr), the instant claims are not analogous to the claims in Diamond v. Diehr (or Example 25) because the instant claims do not recite any meaningful limitations to which the judicial exceptions are applied, whereas in Diamond v. Diehr, the court evaluated additional elements such as the steps of installing rubber in a press, closing the mold, constantly measuring the temperature in the mold, and automatically opening the press at the proper time, and found these additional elements to be meaningful because they sufficiently limited the use of the mathematical equation to the practical application of molding rubber products, i.e., the additional elements (e.g., the press mold) use and/or interact with the judicial exception (i.e., the repeated calculations of the cure time by use of the Arrhenius equation) in a way that integrates the recited judicial exceptions into a practical application.
Claim Rejections - 35 USC § 103
The rejection of claims 1-5 under 35 U.S.C. 103 as being unpatentable over Narayanan et al. in the Office action mailed 28 May 2026 has been withdrawn in view of the amendment received 28 May 2026.
The rejection of claims 9 and 10 under 35 U.S.C. 103 as being unpatentable over Narayanan et al. as applied to claims 1-5 above, and further in view of Sun et al. and Tan et al. in the Office action mailed 28 May 2026 has been withdrawn in view of the amendment received 28 May 2026.
The rejection of claims 6-8, 13, and 14 under 35 U.S.C. 103 as being unpatentable over Narayanan et al. as applied to claims 1-5 above, and further in view of Moutsatsou et al. in the Office action mailed 28 May 2026 has been withdrawn in view of the amendment received 28 May 2026.
The rejection of claims 11 and 12 under 35 U.S.C. 103 as being unpatentable over Narayanan et al. as applied to claims 1-5 above, and further in view of Zalai et al. in the Office action mailed 28 May 2026 has been withdrawn in view of the amendment received 28 May 2026.
The Applicant’s amendment received 03 August 2026 has been fully considered, however after further consideration, new grounds of rejection are raised in view of the amendment.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. (“Hybrid models based on machine learning and an increasing degree of process knowledge: Application to cell culture processes.” Industrial & Engineering Chemistry Research, 2022, vol. 61, pp. 8658-8672, as cited in the Office action mailed 28 May 2026) and Liu et al. (US 2022/0122690, newly cited).
Independent claim 1 broadly encompasses a method for generating one or more integrated models comprising one or more machine learning models and one or more mechanistic models; optimizing the one or more integrated models; and outputting one or more predictive models for developing or operating a process for a cell or gene therapy.
Dependent claims 2-5 further define aspects of the method steps of generating the one or more integrated models, e.g., particular attributes and properties of the models; arranging the models; and constraining a prediction.
Narayanan et al. teaches hybrid models based on machine learning models (i.e., data-driven) and mechanistic (i.e., process knowledge) models for cell culture process modeling.
Lie et al. teaches using an attention-based machine learning model to generate an output that can be used for generating a pharmaceutical composition.
Regarding independent claim 1, Narayanan et al. shows generating a family of hybrid models for cell culture process modeling with varying fractions of process knowledge explicitly encoded in the model, defined as the degree of hybridization, with the two extremes being fully data driven (0%) and fully mechanistic (100%) models (Abstract); comparing the different models based on different metrics: model accuracy, the experimental effort for model development, extrapolation capability, the capability of generating new process understanding, and ease of utilization in practice, and to demonstrate that this could provide an additional degree of freedom for model selection (Abstract). Narayanan et al. further shows key characteristics of in silico and experimental data (Figure 1(A)) and dynamic profiles of the key process variables constituting the experimental data set (Figure 1(C)); the focus is on the biopharmaceutical cell culture processes (page 8659, col. 1, para. 3) and generating a family of hybrid models which incorporate process knowledge and engineering know-how to different extents, i.e., the degree of hybridization (page 8659, col. 2, bottom); a summary of the different models along a Hybridization Axis (Table 1); the design factors and the data collected during the runs (page 8660, Section 2.1.); generation of seven possible hybrid models that reflect attractive possibilities to utilize available knowledge and commonly measured variables in the context of cell cultures (page 8661, col. 2, Section 2.2.); different statistical methods can be used within the hybrid model framework (page. 8662, col. 1, para. 2); different data-driven models, e.g., artificial neural networks (ANNs) (page 8662, col. 1, Section 2.2.1.); hybrid models with inputs to the neural network (page 8662, col. 1, Section 2.2.2.); nonlinear parameter optimization is performed to obtain the parameters for the mechanistic models (page 8662, col. 2, Section 2.2.3.); and output of predictive models with the best predictive capability (page 8664, col. 1, para. 2). Finally, Narayanan et al. shows that the choice of the hybrid models to be used is based on the goal of model development (e.g., the figure in the Abstract).
Regarding independent claim 1, Narayanan et al. does not show the exact sequence and steps for generating the hybrid (i.e., integrated) models; or causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters.
Regarding independent claim 1, Liu et al. shows using an attention-based machine learning model to generate predictions about the immunological activity relating to peptides and immunoprotein complexes (IPCs), wherein the model outputs at least one of an interaction prediction, an interaction affinity prediction, or an immunogenicity prediction for a corresponding peptide-IPC combination, and then a report is generated based on the output (para. [0062]); the report that is generated may be used to plan for, design, and/or manufacture a treatment (para. [0063]); and initiating an action that facilitates manufacture of the individualized vaccine (para. [0476]).
Regarding dependent claims 2-5, Narayanan et al. shows that the hybrid models are solved using a nonlinear parameter estimation problem based on a quasi-Newton optimization algorithm (page 8662, col. 2., para. 1); key process variables include glucose, lactose, NH4, osmolality, and titer (Figure 1(C)); the sequential arrangement of different mechanisms within the hybrid model (Table 1); and adding physical constraints to the model to improve the model’s performance (page 8665, col. 1, para. 3).
Regarding independent claim 1 and dependent claims 2-5, Narayanan et al. does not show the exact sequence and steps for generating the hybrid (i.e., integrated) models.
However, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. by broadly incorporating essentially the same steps but in a different sequence, as shown by Narayanan et al. and discussed above. One of ordinary skill in the art would have been motivated to modify the methods of Narayanan et al. because Narayanan et al. shows methods for generating a range of hybrid models, allowing for a choice of hybrid models to be used that is based on the goal of model development, e.g., models with a higher degree of hybridization allow for more process interpretation possibilities. This modification would have had a reasonable expectation of success given that Narayanan et al. discloses methods for generating hybrid models comprising machine learning models and mechanistic models for the specific application to cell culture processes.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. by incorporating methods for using the machine learning output to initiate an action that facilitates the manufacture of an individualized therapeutic, as shown by Liu et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. with the methods of Liu et al., because Liu et al. shows using a machine learning model to design an individualized vaccine for a subject (para. [0018]) and using the output of the machine learning model for manufacturing a treatment for the subject (para. [0019]). This modification would have had a reasonable expectation of success given that Liu et al. shows using machine learning to design a personalized vaccine and manufacturing the personalized vaccine, and Narayanan et al. shows that hybrid machine learning models can incorporate process knowledge about complex processes such as cell cultures, and concludes that hybrid models are better options than purely data-driven or mechanistic models for cell cultures.
Claims 9 and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. Liu et al. as applied to claims 1-5 above, and further in view of Sun et al. (“ALVEN: Algebraic learning via elastic net for static and dynamic nonlinear model identification.” Computers and Chemical Engineering, 2020, vol. 143, article 107103, pp. 1-9, as cited in the Information Disclosure Statement received 10 January 2023, and as cited in the Office action mailed 28 May 2026) and Tan et al. (“HEK293 cell line as a platform to produce recombinant proteins and viral vectors.” Frontiers in Bioengineering and Biotechnology, 2021, vol. 9, article 796991, pp. 1-9, as cited above, and as cited in the Office action mailed 28 May 2026).
Dependent claim 9 is encompasses one or more algebraic learning via elastic net (ALVEN) output models for use in the production of a stable cell line.
Dependent claim 10 further defines the type of stable cell line as HEK293 cells.
Sun et al. teaches an algebraic learning via elastic net (ALVEN) for static and dynamic nonlinear model identification algorithm that employs automated feature generation including families of ubiquitous chemical and biological nonlinear transformation.
Tan et al. teaches the use of HEK293 cells and its subtypes in the production of biotherapeutics.
Regarding dependent claims 9 and 10, Narayanan et al. and Liu et al. as applied to claims 1-5 above, does not show using an algebraic learning via elastic net (ALVEN) algorithm for output models; or production of a stable cell line comprising HEK293 cells.
Regarding dependent claim 9, Sun et al. shows an ALVEN algorithm that balances model complexity and prediction accuracy through a two-step feature selection procedure, to produce an interpretable model useful for process applications while avoiding overfitting, and that can be generalizable to nonlinear dynamic systems (i.e., Dynamic ALVEN) (Abstract). Sun et al. further shows comparing the model accuracy of the algorithms to well-established machine learning methods for a chemical reactor (Abstract).
Regarding dependent claim 10, Tan et al. shows that since 2015, there have been seven HEK-derived products approved by the FDA, and of these, six are cell and gene therapies where the HEK293 cell line or its derivatives were used in the production of viral vectors (page 1, Introduction; and Table 1). Tan et al. further shows that stable producer lines are preferred for the large-scale production of recombinant proteins (page 2, col. 1, bottom, and col. 2, top).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. and Liu et al. as applied to claims 1-5 above, by incorporating methods for using an algorithm to balance model complexity and prediction accuracy, as shown by Sun et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. and Liu et al. as applied to claims 1-5 above, with the methods of Sun et al., because Sun et al. shows that the ALVEN (algebraic learning via elastic net) algorithm aids in producing an interpretable model useful for process application while avoiding overfitting. This modification would have had a reasonable expectation of success given that both Narayanan et al. and Liu et al. as applied to claims 1-5 above, and Sun et al. disclose methods for improving the predictive capability of a model that is applicable to cell culture processes.
It would have been further prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. and Liu et al. as applied to claims 1-5 above, by incorporating methods for using a stable cell line such as HEK293 cells, as shown by Tan et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. and Liu et al. as applied to claims 1-5 above, with the methods of Tan et al., because Tan et al. shows that stable producer cell lines are preferred for large-scale production processes. This modification would have had a reasonable expectation of success given that both Narayanan et al. and Liu et al. as applied to claims 1-5 above, and Tan et al. disclose methods applicable to cell culture processes.
Claims 6-8, 13, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. and Liu et al. as applied to claims 1-5 above, and further in view of Moutsatsou et al. (“Automation in cell and gene therapy manufacturing: from past to future.” Biotechnology Letters, 2019, vol. 41, pp. 1245-1253, as cited above, and as cited in the Office action mailed 28 May 2026).
Dependent claims 6-8 further define characteristics of the cell populations.
Dependent claims 13 and 14 further define aspects of the production equipment.
Moutsatsou et al. teaches how automation can help address the manufacturing issues arising from the development of large-scale manufacturing processes for modern cell and gene therapy.
Regarding dependent claims 6-8, 13, and 14, Narayanan et al. and Liu et al. as applied to claims 1-5 above, does not show cell populations, or cell populations of different types, or heterogeneous cell populations, or clonal cell populations, or intracellular or cell surface heterogeneity; and does not show whether the production process is automated or semi-automated, or a closed or semi-closed system.
Regarding dependent claims 6-8, Moutsatsou et al. shows a fully automated production unit for reprogramming, cultivation and differentiation of induced pluripotent stems cells (iPSCs) with a capability to process up to 60 different iPSC lines in parallel (page 1250, col. 1, para. 4) (i.e., iPSCs can be heterogeneous cultures or clonal cultures); and a fully automated manufacturing and banking of cell therapies that is a fully enclosed platform (page 1250, col. 1, para. 5). Moutsatsou et al. further shows that another important advantage of 2nd generation automated cell manufacturing platforms is the increased flexibility and modularity, particularly hardware modules that are integrated in the platform via agents into the control software using a plug-and-produce approach and software that is adaptable to different applications (i.e., different process conditions or different cells) (page 1250, col. 2, para. 4). Moutsatsou et al. further shows examples of closed, automated production systems (page 1250, col. 1, paras. 2-3).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. and Liu et al. as applied to claims 1-5 above, by incorporating methods for automation in cell and gene therapy manufacturing, as shown by Moutsatsou et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. and Liu et al. as applied to claims 1-5 above, with the methods of Moutsatsou et al., because Moutsatsou et al. shows methods for automation that can provide more control over a bioprocess while leading to a more accurate and faster process optimization. This modification would have had a reasonable expectation of success given that both Narayanan et al. and Liu et al. as applied to claims 1-5 above, and Moutsatsou et al. disclose methods applicable to cell culture processes.
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Narayanan et al. and Liu et al. as applied to claims 1-5 above, and further in view of Zalai et al. (“Advanced development strategies for biopharmaceutical cell culture processes.” Current Pharmaceutical Biotechnology, 2015, vol. 16, pp. 983-1001, as cited above, and as cited in the Office action mailed 28 May 2026).
Dependent claims 11 and 12 further define aspects of the production process, e.g., the scale of a production run; the type of process modes.
Zalai et al. teaches advanced development strategies for biopharmaceutical cell culture processes, with a focus on tools which enable the integration of physiological knowledge into cell culture process development.
Regarding dependent claims 11 and 12, Narayanan et al. and Liu et al. as applied to claims 1-5 above, does not show the scale of a production run; or the types of process control modes.
Regarding dependent claims 11 and 12, Zalai et al. shows technological process control strategies (page 994, col. 1, para. 2) including: batch process mode (page 994, col. 1, para. 3); fed-batch process (page 994, col. 1, para. 4); and continuous/perfusion process (page 994, col. 1, paras. 5-6). Zalai et al. further shows using real-time measurements in bioprocesses via in-situ Raman spectroscopy across different processing scales, from 3 L up to 2000 L (page 993, col. 1, para. 1).
Therefore, it would have been prima facie obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method shown by Narayanan et al. and Liu et al. as applied to claims 1-5 above, by incorporating methods for incorporating process tools which enable the integration of physiological knowledge into cell culture process development, as shown by Zalai et al. and discussed above. One of ordinary skill in the art would have been motivated to combine the methods of Narayanan et al. and Liu et al. as applied to claims 1-5 above, with the methods of Zalai et al., because Zalai et al. shows methods for advanced process development approaches that can be applied to maximize process performance and to generate process understanding. This modification would have had a reasonable expectation of success given that both Narayanan et al. and Liu et al. as applied to claims 1-5 above, and Zalai et al. disclose methods for cell culture processes.
Response to Arguments
The Applicant’s arguments/remarks received 03 August 2026 have been fully considered, but are not persuasive.
The Applicant provides the amended claim 1 with mark-ups on pages 8-9 of the Remarks (as filed) and states on page 9 that Narayanan neither describes nor renders obvious “responsive to the receiving of the plurality of process data items [with a process data item specifying a process for producing a cell or gene therapy], selecting one or more machine learning models based on the one or more attributes of the plurality of process data items … applying the one or more predictive models to the plurality of process data items with the one or more attributes used to select the one or more machine learning models.” The Applicant points to Section 2.2.1. of Narayanan and states on page 10 (para. 1) of the Remarks that in contrast to Narayanan, the Applicant’s claim 1 requires that process data items are received and that selection of a machine learning model is based on one or more attributes of the process data items, and further states that nowhere does Narayanan describe or render obvious that it considers the BWU-PLS1 model ‘representative’ based on attributes of the training sets themselves, and further states that moreover, another passage from Narayanan actually describes that the models it selects to construct the hybrid models are based on Narayanan’s ‘best judgement.’ The Applicant further states that as shown at Section 2.2 of Narayanan, the model used for the hybrid model is based on ‘best judgement’ and not based on one or more attributes of the process data items as required by the Applicant’s claims. The Applicant further states that moreover, claim 1 requires that the one or more predictive models are applied to the process data items with the one or more attributes that are used to select the one or more machine learning models, and further states that nowhere does Narayanan describe or render obvious that it identifies the BWU-PLS1 model based on attributes of process data items to which Narayanan’s hybrid model is applied, and in fact, Narayanan describes (at Section 2.2.1) the opposite by describing that it chooses the BWU-PLS1 model ‘pre-analysis.’
These arguments are not persuasive, because first, a new combination of references has been used in view of the amendments, in particular with regard to the new limitation reciting “causing a development or manufacturing process to produce a cell or gene therapy in accordance with the one or more predictive models with the one or more adjusted values of the parameters.” Second, Narayanan et al. explicitly states that hybrid models can be created with varying fractions of process knowledge explicitly encoded in the model, defined as the degree of hybridization, with the two extremes being fully data-driven (0% process knowledge) and fully mechanistic (100% process knowledge) models (Abstract). Third, regarding Section 2.2.1. of Narayanan et al. and the BWU-PLS1 model that was chosen based on a ‘pre-analysis,’ it is noted that Narayanan et al. recognizes this model to be ‘representative’ of the data-driven (DD) approach, i.e., the different types of machine learning models chosen prior to hybridization with a mechanistic model. Fourth, Narayanan et al. states at Section 2.2. that consideration is given to seven possible hybrid models characterized by a different degree of hybridization (Table 1), and notes that among the possible infinite choices to construct hybrid models, Narayanan et al. selected those that, in their best judgement, reflect attractive possibilities to utilize available knowledge and commonly measured variables in the context of cell cultures. Fifth, Narayanan et al. states that the degree of hybridization signifies the amount of process knowledge (i.e., process data) and engineering know-how that is described explicitly in the mechanistic backbone of the hybrid models, and explains that the degree of hybridization correlates with the degree of process variable data that the model interacts with, e.g., a purely data-driven model (0% hybridized) has no process data incorporated and, thus, tries to predict the process variables directly, whereas the Hyb-No MB model incorporates the rate of accumulation mechanism (i.e., process variable data) into the prediction (Table 1; and page 8661, col. 2, para. 3).
Thus, the instant claimed invention would have been prima facie obvious before the effective filing date of the claimed invention in view of the combination of Narayanan et al. and Liu et al.
Conclusion
No claims are allowed.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/S.W.B./Examiner, Art Unit 1687
/Joseph Woitach/Primary Examiner, Art Unit 1687
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